1. Field of the Invention
The present invention relates to an information processing method and an apparatus relating to a classification method for classifying patterns such as images, sounds and characters into categories.
The present invention further relates to an information processing method and an apparatus suitable for recognizing information of images, sounds and characters.
2. Related Background Art
In a prior art method of pattern recognition, a recognition method using a neural network has been known. A great feature of the neural network resides in that a powerful learn algorithm represented by an error inverse propagation algorithm is provided. Because of this feature, the neural network has been adopted in a wide field of pattern recognition.
Another method for pattern recognition is a method for classifying a pattern into a category by using classification trees stepwise. For example, in the pattern recognition system disclosed in JP-B-6-52537, characteristic axes are numbered and they are categorized in accordance with the numbers.
A method for categorizing based on primary coupling of characteristic variables is also known. In general, a better result is provided by using the primary coupling of characteristic variables than using the characteristic axes one by one.
However, the above prior art techniques have the following defects.
1. The range of the characteristic variables to which the neural network is applicable is in the order of 10, and when input variables include higher order variables, some category pre-separation or character extraction is needed. In addition, when pre-processing such as category pre-separation or character extraction is conducted, an error may be included during the pre-processing and a final recognition rate is not that high even if the neural network is constructed with a high precision.
2. The range of character variables to which the classification trees are applicable is also in the order of 10, and when higher order characteristic variables are to be processed, the construction of the classification trees is virtually impossible.
3. In the actual pattern recognition, the orders of character variables of unprocessed data ranges between 100 and 1000. Thus, it is impossible to use the existing neural network and the classification trees which allow only the order of 10 to the actual pattern recognition as they exist.